File size: 3,227 Bytes
be903e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | // Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2023 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.
#include "fuse_layernorm.h"
#include "pass_level2.h"
#include <math.h>
#include <string.h>
#include <torch/csrc/api/include/torch/torch.h>
namespace pnnx {
class fuse_layernorm_pass : public GraphRewriterPass
{
public:
const char* match_pattern_graph() const
{
return R"PNNXIR(7767517
8 7
pnnx.Input input 0 1 input #input=(1,%c,?,?)f32
pnnx.Attribute op_0 0 1 weight @data #weight=(%c,1,1)f32
pnnx.Attribute op_1 0 1 bias @data #bias=(%c,1,1)f32
torch.mean op_2 1 1 input mean dim=(1) keepdim=True
pnnx.Expression op_3 2 1 input mean 2 expr=pow(sub(@0,@1),2)
torch.mean op_4 1 1 2 var dim=(1) keepdim=True
pnnx.Expression op_5 5 1 weight input mean var bias out expr=add(mul(@0,div(sub(@1,@2),sqrt(add(@3,%eps)))),@4)
pnnx.Output output 1 0 out
)PNNXIR";
}
const char* replace_pattern_graph() const
{
#if TORCH_VERSION_MAJOR >= 2 || TORCH_VERSION_MAJOR == 1 && TORCH_VERSION_MINOR >= 9
return R"PNNXIR(7767517
5 4
pnnx.Input input 0 1 input
torch.permute op_0 1 1 input a dims=(0,2,3,1)
nn.LayerNorm op_1 1 1 a b elementwise_affine=True eps=%eps normalized_shape=(%c) @weight=%op_0.data @bias=%op_1.data
torch.permute op_2 1 1 b out dims=(0,3,1,2)
pnnx.Output output 1 0 out
)PNNXIR";
#else
return R"PNNXIR(7767517
5 4
pnnx.Input input 0 1 input
Tensor.permute op_0 1 1 input a dims=(0,2,3,1)
nn.LayerNorm op_1 1 1 a b elementwise_affine=True eps=%eps normalized_shape=(%c) @weight=%op_0.data @bias=%op_1.data
Tensor.permute op_2 1 1 b out dims=(0,3,1,2)
pnnx.Output output 1 0 out
)PNNXIR";
#endif
}
void write(const std::map<std::string, Operator*>& ops, const std::map<std::string, Parameter>& captured_params, const std::map<std::string, Attribute>& captured_attrs) const
{
GraphRewriterPass::write(ops, captured_params, captured_attrs);
// fix weight bias shape from (c,1,1) to (c)
const int c = captured_params.at("c").i;
Operator* op_1 = ops.at("op_1");
op_1->attrs["weight"].shape = {c};
op_1->attrs["bias"].shape = {c};
}
};
void fuse_layernorm(Graph& graph)
{
fuse_layernorm_pass a;
int opindex = 0;
pnnx_graph_rewrite(graph, &a, opindex);
}
} // namespace pnnx
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